LLM-as-a-Verifier upgrades "scoring" from one-off LLM-as-Judge to a scalable training objective — 9 scholars including Ion Stoica, Chelsea Finn, and Azalia Mirhoseini posted arXiv 2607.05391 on July 6, and the GitHub repo got 409 stars in a week. The core insight isn't complex: after pre-training, post-training, and test-time compute, verification (judging "right or wrong") is the next axis that should be scaled. Traditional LLM-as-Judge outputs discrete scores, signal too coarse to have a gradient to learn. This paper changes the accounting method — taking the expectation of scoring-token logits to get a continuous score; at the same time splitting verification into three axes: score granularity (finer score granularity means better positive/negative separation), repeated evaluation (multiple evaluations reduce variance), criteria decomposition (split the judging criteria to score separately). All three axes turning together gives Terminal-Bench V2 86.5%, SWE-Bench Verified 78.2%, RoboRewardBench 87.4%, MedAgentBench 73.3% — SOTA across 4 unrelated domains. The landing posture is even more interesting: the team built an extension for Claude Code, directly feeding the verifier to the agent's process-level feedback, equivalent to stuffing an "accuracy radar" into the self-correction loop; at the same time using continuous scores as RL dense reward, the sample efficiency of SAC, GRPO on robotics and math reasoning visibly improves. verification moves from "scoring tool" to "training signal source". A deeper paradigm: when scaling law hits energy and compute walls at the pretraining end, the industry has been looking for the next scalable variable. post-training and test-time compute have been validated by RLHF and o1-style reasoning models; with verification stepping in, LLMs are no longer just "answerers", but begin to evolve toward a "set-question-answer-judge" closed loop. The Claude Code section is currently the most engineering-flavored sample on this path.